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Behavioural Responses to Robot Conversational Failures

Time: Tue 2020-03-10 15.15

Location: Fantum (Lindstedtsvägen 24, floor 5, room no. 522)

Participating: Dimosthenis Kontogiorgos

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Humans and robots will increasingly collaborate in domestic environments
which will cause users to encounter more failures in interactions.
Robots should be able to infer conversational failures by detecting
human users’ behavioural and social signals. In this paper, we study and
analyse these behavioural cues in response to robot conversational
failures. Using a guided task corpus, where robot embodiment and time
pressure are manipulated, we ask human annotators to estimate whether
user affective states differ during various types of robot failures. We
also train a random forest classifier to detect whether a robot failure
has occurred and compare results to human annotator benchmarks. Our
findings show that human-like robots augment users’ reactions to
failures, as shown in users’ visual attention, in comparison to
non-human-like smart-speaker embodiments. The results further suggest
that speech behaviours are utilised more in responses to failures when
non-human-like designs are present. This is particularly important to
robot failure detection mechanisms that may need to consider the robot’s
physical design in its failure detection model.